code: def before_train(
      loaded_train_model,
      train_model,
      train_sess,
      global_step,
      hparams,
      log_f,
  ):
      """Misc tasks to do before training."""
      stats = init_stats()
      info = {
          "train_ppl": 0.0,
          "speed": 0.0,
          "avg_step_time": 0.0,
          "avg_grad_norm": 0.0,
          "avg_sequence_count": 0.0,
          "learning_rate": loaded_train_model.learning_rate.eval(
              session=train_sess
          ),
      }
      start_train_time = time.time()
      utils.print_out(
          "# Start step %d, lr %g, %s"
          % (global_step, info["learning_rate"], time.ctime()),
          log_f,
      )
  
      # Initialize all of the iterators
      skip_count = hparams.batch_size * hparams.epoch_step
      utils.print_out(
          "# Init train iterator, skipping %d elements" % skip_count
      )
      train_sess.run(
          train_model.iterator.initializer,
          feed_dict={train_model.skip_count_placeholder: skip_count},
      )
  
      return stats, info, start_train_time
  
imports: from __future__ import print_function
  import math
  import os
  import random
  import time
  import tensorflow
  from .None import inference, gnmt_model
  from .gnmt import attention_model, model
  from .gnmt import model_helper
  from .gnmt.utils import nmt_utils, misc_utils
label_desc: Use image node_id 12 for calling a global function with example usage: before_train(loaded_train_model, train_model, train_sess, global_step, hparams, log_f) and returns: stats,  info,  start_train_time